How do capsule neural networks work?
It appears to not be published yet; the best available online are these slides for this talk. (Several people reference an earlier talk with this link, but sadly it's broken at time of writing this answer.)

My impression is that it's an attempt to formalize and abstract the creation of subnetworks inside a neural network. That is, if you look at a standard neural network, layers are fully connected (that is, every neuron in layer 1 has access to every neuron in layer 0, and is itself accessed by every neuron in layer 2). But this isn't obviously useful; one might instead have, say, n parallel stacks of layers (the 'capsules') that each specializes on some separate task (which may itself require more than one layer to complete successfully).

If I'm imagining its results correctly, this more sophisticated graph topology seems like something that could easily increase both the effectiveness and the interpretability of the resulting network.